Federated learning (FL) is a form of decentralized machine learning, in which a global model is formed by an orchestration server aggregating the outcome of training on a number of local client models without any sharing of their private training data [1]}. Interest in federated learning has increased recently for its privacy and communication-efficiency advantages over centralized learning on mobile and edge devices [2]}, [3]}. A classical mechanism for model aggregation in FL is federated averaging (FedAVG), where the locally trained models are weighted proportionally to the size of the local dataset. In each communication round of federated averaging, weight updates of the clients' local models are sent to the orchestration server, averaged by the server, and the average is sent back to the federation of clients to initialize the next round of training [1]}.
